{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.利用 pandas 读取 csv 格式数据文件，去除其中的重复值，并展示前部数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('jobs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>company</th>\n",
       "      <th>company_size</th>\n",
       "      <th>industry</th>\n",
       "      <th>type</th>\n",
       "      <th>salary</th>\n",
       "      <th>company_type</th>\n",
       "      <th>source</th>\n",
       "      <th>experience</th>\n",
       "      <th>education</th>\n",
       "      <th>salary2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>招聘数据分析师</td>\n",
       "      <td>北京越铖国际科技有限公司</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>人事/行政/高级管理</td>\n",
       "      <td>全职</td>\n",
       "      <td>8000-12000</td>\n",
       "      <td>民营</td>\n",
       "      <td>斗米</td>\n",
       "      <td>不限</td>\n",
       "      <td>不限</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京新东方教育科技（集团）有限公司</td>\n",
       "      <td>NaN</td>\n",
       "      <td>计算机/互联网/信息技术</td>\n",
       "      <td>全职</td>\n",
       "      <td>12000-20000</td>\n",
       "      <td>民营</td>\n",
       "      <td>OFweek人才网</td>\n",
       "      <td>不限</td>\n",
       "      <td>不限</td>\n",
       "      <td>16000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>数据分析师(新浪)</td>\n",
       "      <td>新浪网技术(中国)有限公司</td>\n",
       "      <td>2000-5000人</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>全职</td>\n",
       "      <td>20000-25000</td>\n",
       "      <td>外商独资/办事处</td>\n",
       "      <td>猎聘</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>22500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>北京美科思远环境科技有限公司</td>\n",
       "      <td>20-99人</td>\n",
       "      <td>环保/环境科学类</td>\n",
       "      <td>全职</td>\n",
       "      <td>12000-20000</td>\n",
       "      <td>股份制</td>\n",
       "      <td>北极星招聘</td>\n",
       "      <td>不限</td>\n",
       "      <td>硕士</td>\n",
       "      <td>16000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>助理、临床样本库采样人员、软件工程师、数据库管理工程师、机械设计与结构工程师、Android...</td>\n",
       "      <td>中国人民解放军总医院（北京市解放军医学院）</td>\n",
       "      <td>NaN</td>\n",
       "      <td>医疗</td>\n",
       "      <td>全职</td>\n",
       "      <td>面议</td>\n",
       "      <td>公立医院</td>\n",
       "      <td>康强医疗人才网</td>\n",
       "      <td>不限</td>\n",
       "      <td>不限</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title                company  \\\n",
       "0                                            招聘数据分析师           北京越铖国际科技有限公司   \n",
       "1                                              数据分析师      北京新东方教育科技（集团）有限公司   \n",
       "2                                          数据分析师(新浪)          新浪网技术(中国)有限公司   \n",
       "3                                              数据分析师         北京美科思远环境科技有限公司   \n",
       "4  助理、临床样本库采样人员、软件工程师、数据库管理工程师、机械设计与结构工程师、Android...  中国人民解放军总医院（北京市解放军医学院）   \n",
       "\n",
       "  company_size      industry type       salary company_type     source  \\\n",
       "0       20-99人    人事/行政/高级管理   全职   8000-12000           民营         斗米   \n",
       "1          NaN  计算机/互联网/信息技术   全职  12000-20000           民营  OFweek人才网   \n",
       "2   2000-5000人         数据分析师   全职  20000-25000     外商独资/办事处         猎聘   \n",
       "3       20-99人      环保/环境科学类   全职  12000-20000          股份制      北极星招聘   \n",
       "4          NaN            医疗   全职           面议         公立医院    康强医疗人才网   \n",
       "\n",
       "  experience education  salary2  \n",
       "0         不限        不限  10000.0  \n",
       "1         不限        不限  16000.0  \n",
       "2       3-5年        本科  22500.0  \n",
       "3         不限        硕士  16000.0  \n",
       "4         不限        不限      NaN  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 650 entries, 0 to 649\n",
      "Data columns (total 11 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   title         647 non-null    object \n",
      " 1   company       650 non-null    object \n",
      " 2   company_size  588 non-null    object \n",
      " 3   industry      642 non-null    object \n",
      " 4   type          650 non-null    object \n",
      " 5   salary        650 non-null    object \n",
      " 6   company_type  650 non-null    object \n",
      " 7   source        647 non-null    object \n",
      " 8   experience    650 non-null    object \n",
      " 9   education     650 non-null    object \n",
      " 10  salary2       546 non-null    float64\n",
      "dtypes: float64(1), object(10)\n",
      "memory usage: 60.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>546.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14409.894689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5933.391289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            salary2\n",
       "count    546.000000\n",
       "mean   14409.894689\n",
       "std     5933.391289\n",
       "min     2500.000000\n",
       "25%    10000.000000\n",
       "50%    16000.000000\n",
       "75%    16000.000000\n",
       "max    25000.000000"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.统计数据表中的招聘岗位都来自于哪些网站"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "猎聘           580\n",
       "OFweek人才网     33\n",
       "斗米            17\n",
       "北极星招聘          5\n",
       "百姓网            5\n",
       "普工招聘网          4\n",
       "NaN            3\n",
       "工厂直聘网          2\n",
       "康强医疗人才网        1\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['source'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.统计数据分析师这个岗位，对于工作经验的要求"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-5年     232\n",
       "1-3年     197\n",
       "不限       142\n",
       "5-10年     75\n",
       "10年以上      4\n",
       "Name: experience, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['experience'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.统计该地区数据分析岗位对于教育背景的要求"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "本科    489\n",
       "不限     71\n",
       "硕士     54\n",
       "大专     26\n",
       "初中      9\n",
       "博士      1\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['education'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.请统计出不同类型公司的职位数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "民营          458\n",
       "上市公司         62\n",
       "外商独资/办事处     45\n",
       "中外合资/合作      42\n",
       "国企           34\n",
       "事业单位          4\n",
       "个人企业          2\n",
       "股份制           1\n",
       "其它            1\n",
       "公立医院          1\n",
       "Name: company_type, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['company_type'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.所有职位的平均工资是多少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14409.894688644688"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['salary2'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.请统计平均薪资（即 salary2）排在前 10 位的招聘公司及金额"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_salary_mean = pd.DataFrame(df_new['salary2'].groupby(df_new['company']).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>xx公司</th>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>欧科互动网络科技(北京)有限公司</th>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中商利高科技(北京)有限公司</th>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京某互联网公司</th>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中建材信息技术股份有限公司</th>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京奇元科技有限公司</th>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>某互联网公司</th>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>清控紫荆(北京)教育科技股份有限公司</th>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中网数据(北京)股份有限公司</th>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京城市网邻信息技术有限公司</th>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    salary2\n",
       "company                    \n",
       "xx公司                25000.0\n",
       "欧科互动网络科技(北京)有限公司    25000.0\n",
       "中商利高科技(北京)有限公司      25000.0\n",
       "北京某互联网公司            25000.0\n",
       "中建材信息技术股份有限公司       25000.0\n",
       "北京奇元科技有限公司          25000.0\n",
       "某互联网公司              25000.0\n",
       "清控紫荆(北京)教育科技股份有限公司  25000.0\n",
       "中网数据(北京)股份有限公司      25000.0\n",
       "北京城市网邻信息技术有限公司      25000.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_salary_mean.sort_values('salary2',ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.请统计发布职位数排在前 10 位的招聘公司及其职位数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_work_num = pd.DataFrame(df_new['salary2'].groupby(df_new['company']).count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>车好多旧机动车经纪(北京)有限公司</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>深圳索信达数据技术有限公司</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>海南小易到家电子商务有限公司上海分公司</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>苏州皇亭电子有限公司</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广联达科技股份有限公司</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京友信科技有限公司</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京百分点信息科技有限公司</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京中油瑞飞信息技术有限责任公司</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马上消费金融股份有限公司</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京嘀嘀无限科技发展有限公司</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     salary2\n",
       "company                     \n",
       "车好多旧机动车经纪(北京)有限公司          7\n",
       "深圳索信达数据技术有限公司              7\n",
       "海南小易到家电子商务有限公司上海分公司        6\n",
       "苏州皇亭电子有限公司                 5\n",
       "广联达科技股份有限公司                4\n",
       "北京友信科技有限公司                 4\n",
       "北京百分点信息科技有限公司              4\n",
       "北京中油瑞飞信息技术有限责任公司           4\n",
       "马上消费金融股份有限公司               4\n",
       "北京嘀嘀无限科技发展有限公司             4"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_work_num.sort_values('salary2',ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.请统计出平均工资排在前 10 位的公司类型及平均工资金额"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_company_salary_mean = pd.DataFrame(df_new['salary2'].groupby(df_new['company_type']).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company_type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>外商独资/办事处</th>\n",
       "      <td>17468.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股份制</th>\n",
       "      <td>16000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>民营</th>\n",
       "      <td>14378.595718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上市公司</th>\n",
       "      <td>14367.346939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>事业单位</th>\n",
       "      <td>14166.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中外合资/合作</th>\n",
       "      <td>13357.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>个人企业</th>\n",
       "      <td>13250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国企</th>\n",
       "      <td>12846.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其它</th>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>公立医院</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   salary2\n",
       "company_type              \n",
       "外商独资/办事处      17468.750000\n",
       "股份制           16000.000000\n",
       "民营            14378.595718\n",
       "上市公司          14367.346939\n",
       "事业单位          14166.666667\n",
       "中外合资/合作       13357.142857\n",
       "个人企业          13250.000000\n",
       "国企            12846.153846\n",
       "其它            10000.000000\n",
       "公立医院                   NaN"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_company_salary_mean.sort_values('salary2',ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
